Automatic Orchid Bottle Seedling Image Feature Extraction and Measurement based on Deep Mask Regions Convolutional Neural Networks

• Main Authors: Jing-LuneYang, 楊景倫
• Other Authors: Jeen-Shin Wang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/b4s94c

碩士 === 國立成功大學 === 電機工程學系 === 107 === This thesis aims to develop an automatic orchid bottle seedling image feature extraction and measurement algorithms based on mask regions convolutional neural networks (Mask R-CNN) for extracting the important growth features of orchid bottle seedlings to reach the goal of precise cultivation. In this study, to train and test the Mask R-CNN, orchid bottle seedling images from different view angles were obtained from an orchid plantation factory in the southern Taiwan. The original images collected from the factory were first labeled for their outlook contours such leaves and roots. These contours are called as masks. Then, the labeled images were distorted to increase the diversity of the training and testing images. Finally, these images with their corresponding masks were served as the golden standards for the network training. The Mask R-CNN-based image detection algorithm has been developed to extract the features of orchid bottle seedlings, including leaf, root, green root tip, white root tip, yellow leaf, green leaf effectively and automatically. Ten different Mask R-CNN models were constructed for performance comparisons. These ten models are the different layers of residual network (ResNet) including ResNet-26, ResNet-41, ResNet-50, ResNet-101, and ResNet-152 combined with fully convolutional network (FCN) and U-network (UNet), respectively. The experimental results show that the ResNet-101-UNet outperforms the other models with higher average precision (AP) of feature extraction at 77.89%, and its training time is 199 ms/image. In addition to the feature extraction, a feature measurement algorithm has been developed to measure/calculate the features, such as the number of leaves and the length, width, and area of each leaf from orchid bottle seedling images detected by the Mask R-CNN models. The experimental results show that the average percentage error of the area measurement of leaves is 16.47±6.41% due to the shading or blocking by other leaves or curly leaves, while the average percentage error of the length measurement of roots is 7.28±3.01%. The overall average errors of the feature measurements/calculations were satisfactory, and thus validated the effectiveness of the proposed methods for the feature extraction of orchid bottle seedlings. In the future, we hope these algorithms can be applied to the orchid plantation industry and reach the goal of precise cultivation of orchid bottle seedlings.